{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from H2ighwayNet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = HighwayNet().to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 3.9969 Top-1 准确率: 0.0896 Top-5 准确率: 0.2743\n",
      "val 损失: 4.2875 Top-1 准确率: 0.0858 Top-5 准确率: 0.2669\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2437 Top-1 准确率: 0.2052 Top-5 准确率: 0.4911\n",
      "val 损失: 3.3936 Top-1 准确率: 0.1982 Top-5 准确率: 0.4747\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7452 Top-1 准确率: 0.2951 Top-5 准确率: 0.6178\n",
      "val 损失: 3.0524 Top-1 准确率: 0.2630 Top-5 准确率: 0.5774\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4169 Top-1 准确率: 0.3654 Top-5 准确率: 0.6905\n",
      "val 损失: 2.8393 Top-1 准确率: 0.3025 Top-5 准确率: 0.6192\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1817 Top-1 准确率: 0.4145 Top-5 准确率: 0.7410\n",
      "val 损失: 2.3630 Top-1 准确率: 0.3882 Top-5 准确率: 0.7109\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0184 Top-1 准确率: 0.4537 Top-5 准确率: 0.7732\n",
      "val 损失: 2.5375 Top-1 准确率: 0.3736 Top-5 准确率: 0.6956\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8613 Top-1 准确率: 0.4901 Top-5 准确率: 0.8026\n",
      "val 损失: 2.0145 Top-1 准确率: 0.4734 Top-5 准确率: 0.7722\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7394 Top-1 准确率: 0.5204 Top-5 准确率: 0.8227\n",
      "val 损失: 2.2989 Top-1 准确率: 0.4274 Top-5 准确率: 0.7370\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6443 Top-1 准确率: 0.5430 Top-5 准确率: 0.8379\n",
      "val 损失: 2.0349 Top-1 准确率: 0.4729 Top-5 准确率: 0.7757\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5569 Top-1 准确率: 0.5646 Top-5 准确率: 0.8519\n",
      "val 损失: 1.6190 Top-1 准确率: 0.5558 Top-5 准确率: 0.8401\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4767 Top-1 准确率: 0.5840 Top-5 准确率: 0.8657\n",
      "val 损失: 1.7557 Top-1 准确率: 0.5443 Top-5 准确率: 0.8215\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4146 Top-1 准确率: 0.6021 Top-5 准确率: 0.8744\n",
      "val 损失: 1.7030 Top-1 准确率: 0.5393 Top-5 准确率: 0.8263\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3592 Top-1 准确率: 0.6146 Top-5 准确率: 0.8834\n",
      "val 损失: 1.5591 Top-1 准确率: 0.5768 Top-5 准确率: 0.8481\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3023 Top-1 准确率: 0.6275 Top-5 准确率: 0.8914\n",
      "val 损失: 1.7382 Top-1 准确率: 0.5459 Top-5 准确率: 0.8211\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2502 Top-1 准确率: 0.6445 Top-5 准确率: 0.8960\n",
      "val 损失: 1.8174 Top-1 准确率: 0.5306 Top-5 准确率: 0.8170\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2062 Top-1 准确率: 0.6534 Top-5 准确率: 0.9038\n",
      "val 损失: 1.5754 Top-1 准确率: 0.5782 Top-5 准确率: 0.8510\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1511 Top-1 准确率: 0.6684 Top-5 准确率: 0.9113\n",
      "val 损失: 1.4718 Top-1 准确率: 0.5927 Top-5 准确率: 0.8654\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1177 Top-1 准确率: 0.6791 Top-5 准确率: 0.9167\n",
      "val 损失: 1.7188 Top-1 准确率: 0.5490 Top-5 准确率: 0.8269\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0717 Top-1 准确率: 0.6931 Top-5 准确率: 0.9208\n",
      "val 损失: 1.4908 Top-1 准确率: 0.5983 Top-5 准确率: 0.8629\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0431 Top-1 准确率: 0.6980 Top-5 准确率: 0.9262\n",
      "val 损失: 1.4368 Top-1 准确率: 0.6064 Top-5 准确率: 0.8727\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0176 Top-1 准确率: 0.7025 Top-5 准确率: 0.9298\n",
      "val 损失: 1.5381 Top-1 准确率: 0.5957 Top-5 准确率: 0.8635\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9725 Top-1 准确率: 0.7156 Top-5 准确率: 0.9340\n",
      "val 损失: 1.5634 Top-1 准确率: 0.5842 Top-5 准确率: 0.8640\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9318 Top-1 准确率: 0.7271 Top-5 准确率: 0.9406\n",
      "val 损失: 1.5481 Top-1 准确率: 0.6010 Top-5 准确率: 0.8634\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9155 Top-1 准确率: 0.7336 Top-5 准确率: 0.9411\n",
      "val 损失: 1.5170 Top-1 准确率: 0.5971 Top-5 准确率: 0.8704\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8822 Top-1 准确率: 0.7417 Top-5 准确率: 0.9463\n",
      "val 损失: 1.3744 Top-1 准确率: 0.6320 Top-5 准确率: 0.8854\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8507 Top-1 准确率: 0.7490 Top-5 准确率: 0.9478\n",
      "val 损失: 1.4347 Top-1 准确率: 0.6190 Top-5 准确率: 0.8735\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8318 Top-1 准确率: 0.7565 Top-5 准确率: 0.9501\n",
      "val 损失: 1.4790 Top-1 准确率: 0.6173 Top-5 准确率: 0.8816\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7978 Top-1 准确率: 0.7662 Top-5 准确率: 0.9538\n",
      "val 损失: 1.3745 Top-1 准确率: 0.6330 Top-5 准确率: 0.8814\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7837 Top-1 准确率: 0.7676 Top-5 准确率: 0.9568\n",
      "val 损失: 1.3494 Top-1 准确率: 0.6404 Top-5 准确率: 0.8881\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7437 Top-1 准确率: 0.7825 Top-5 准确率: 0.9595\n",
      "val 损失: 1.5330 Top-1 准确率: 0.6058 Top-5 准确率: 0.8664\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7170 Top-1 准确率: 0.7893 Top-5 准确率: 0.9634\n",
      "val 损失: 1.2957 Top-1 准确率: 0.6478 Top-5 准确率: 0.8959\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7114 Top-1 准确率: 0.7885 Top-5 准确率: 0.9624\n",
      "val 损失: 1.3398 Top-1 准确率: 0.6464 Top-5 准确率: 0.8889\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6763 Top-1 准确率: 0.8013 Top-5 准确率: 0.9677\n",
      "val 损失: 1.2494 Top-1 准确率: 0.6669 Top-5 准确率: 0.9035\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6545 Top-1 准确率: 0.8071 Top-5 准确率: 0.9687\n",
      "val 损失: 1.4211 Top-1 准确率: 0.6417 Top-5 准确率: 0.8789\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6482 Top-1 准确率: 0.8081 Top-5 准确率: 0.9695\n",
      "val 损失: 1.4362 Top-1 准确率: 0.6291 Top-5 准确率: 0.8842\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6187 Top-1 准确率: 0.8170 Top-5 准确率: 0.9719\n",
      "val 损失: 1.3482 Top-1 准确率: 0.6547 Top-5 准确率: 0.8890\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5966 Top-1 准确率: 0.8237 Top-5 准确率: 0.9743\n",
      "val 损失: 1.2604 Top-1 准确率: 0.6678 Top-5 准确率: 0.9033\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5778 Top-1 准确率: 0.8296 Top-5 准确率: 0.9745\n",
      "val 损失: 1.3563 Top-1 准确率: 0.6565 Top-5 准确率: 0.8920\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5648 Top-1 准确率: 0.8342 Top-5 准确率: 0.9760\n",
      "val 损失: 1.3942 Top-1 准确率: 0.6509 Top-5 准确率: 0.8847\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5418 Top-1 准确率: 0.8405 Top-5 准确率: 0.9785\n",
      "val 损失: 1.3236 Top-1 准确率: 0.6654 Top-5 准确率: 0.8996\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5199 Top-1 准确率: 0.8484 Top-5 准确率: 0.9800\n",
      "val 损失: 1.4003 Top-1 准确率: 0.6460 Top-5 准确率: 0.8926\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5080 Top-1 准确率: 0.8504 Top-5 准确率: 0.9804\n",
      "val 损失: 1.4704 Top-1 准确率: 0.6371 Top-5 准确率: 0.8804\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4936 Top-1 准确率: 0.8568 Top-5 准确率: 0.9813\n",
      "val 损失: 1.4362 Top-1 准确率: 0.6398 Top-5 准确率: 0.8868\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4811 Top-1 准确率: 0.8575 Top-5 准确率: 0.9821\n",
      "val 损失: 1.2375 Top-1 准确率: 0.6773 Top-5 准确率: 0.9090\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4669 Top-1 准确率: 0.8643 Top-5 准确率: 0.9841\n",
      "val 损失: 1.2617 Top-1 准确率: 0.6799 Top-5 准确率: 0.9025\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4523 Top-1 准确率: 0.8685 Top-5 准确率: 0.9852\n",
      "val 损失: 1.2245 Top-1 准确率: 0.6833 Top-5 准确率: 0.9092\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4286 Top-1 准确率: 0.8755 Top-5 准确率: 0.9864\n",
      "val 损失: 1.3968 Top-1 准确率: 0.6594 Top-5 准确率: 0.8962\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4234 Top-1 准确率: 0.8760 Top-5 准确率: 0.9864\n",
      "val 损失: 1.3170 Top-1 准确率: 0.6772 Top-5 准确率: 0.9031\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4156 Top-1 准确率: 0.8796 Top-5 准确率: 0.9875\n",
      "val 损失: 1.4005 Top-1 准确率: 0.6610 Top-5 准确率: 0.8958\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4102 Top-1 准确率: 0.8806 Top-5 准确率: 0.9876\n",
      "val 损失: 1.2585 Top-1 准确率: 0.6852 Top-5 准确率: 0.9053\n",
      "\n",
      "训练完成，耗时 79m 47s\n",
      "最佳验证集准确率: 0.685200\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:102: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/9 轮训练\n",
      "----------\n",
      "train 损失: 0.2568 Top-1 准确率: 0.9330 Top-5 准确率: 0.9941\n",
      "val 损失: 0.9900 Top-1 准确率: 0.7395 Top-5 准确率: 0.9304\n",
      "\n",
      "第 1/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1990 Top-1 准确率: 0.9526 Top-5 准确率: 0.9960\n",
      "val 损失: 0.9758 Top-1 准确率: 0.7410 Top-5 准确率: 0.9333\n",
      "\n",
      "第 2/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1865 Top-1 准确率: 0.9567 Top-5 准确率: 0.9961\n",
      "val 损失: 0.9855 Top-1 准确率: 0.7399 Top-5 准确率: 0.9322\n",
      "\n",
      "第 3/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1745 Top-1 准确率: 0.9608 Top-5 准确率: 0.9967\n",
      "val 损失: 0.9764 Top-1 准确率: 0.7418 Top-5 准确率: 0.9335\n",
      "\n",
      "第 4/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1646 Top-1 准确率: 0.9627 Top-5 准确率: 0.9968\n",
      "val 损失: 0.9689 Top-1 准确率: 0.7470 Top-5 准确率: 0.9338\n",
      "\n",
      "第 5/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1614 Top-1 准确率: 0.9639 Top-5 准确率: 0.9969\n",
      "val 损失: 0.9693 Top-1 准确率: 0.7417 Top-5 准确率: 0.9341\n",
      "\n",
      "第 6/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1516 Top-1 准确率: 0.9667 Top-5 准确率: 0.9978\n",
      "val 损失: 0.9680 Top-1 准确率: 0.7478 Top-5 准确率: 0.9343\n",
      "\n",
      "第 7/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1510 Top-1 准确率: 0.9670 Top-5 准确率: 0.9972\n",
      "val 损失: 0.9800 Top-1 准确率: 0.7477 Top-5 准确率: 0.9319\n",
      "\n",
      "第 8/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1442 Top-1 准确率: 0.9688 Top-5 准确率: 0.9976\n",
      "val 损失: 0.9512 Top-1 准确率: 0.7496 Top-5 准确率: 0.9356\n",
      "\n",
      "第 9/9 轮训练\n",
      "----------\n",
      "train 损失: 0.1413 Top-1 准确率: 0.9700 Top-5 准确率: 0.9974\n",
      "val 损失: 0.9612 Top-1 准确率: 0.7485 Top-5 准确率: 0.9352\n",
      "\n",
      "训练完成，耗时 16m 45s\n",
      "最佳验证集准确率: 0.749600\n"
     ]
    }
   ],
   "source": [
    "model2,(train_losses2, train_top1_accs2, train_top5_accs2, val_losses2, val_top1_accs2, val_top5_accs2) = train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs = 10)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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